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Article Dans Une Revue Data Mining and Knowledge Discovery Année : 2022

XEM: An explainable-by-design ensemble method for multivariate time series classification

Résumé

We present XEM, an eXplainable-by-design Ensemble method for Multivariate time series classification. XEM relies on a new hybrid ensemble method that combines an explicit boosting-bagging approach to handle the bias-variance trade-off faced by machine learning models and an implicit divide-and-conquer approach to individualize classifier errors on different parts of the training data. Our evaluation shows that XEM outperforms the state-of-the-art MTS classifiers on the public UEA datasets. Furthermore, XEM provides faithful explainability by-design and manifests robust performance when faced with challenges arising from continuous data collection (different MTS length, missing data and noise).
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Dates et versions

hal-03599214 , version 1 (07-03-2022)

Identifiants

Citer

Kevin Fauvel, Elisa Fromont, Véronique Masson, Philippe Faverdin, Alexandre Termier. XEM: An explainable-by-design ensemble method for multivariate time series classification. Data Mining and Knowledge Discovery, 2022, 36 (3), pp.917-957. ⟨10.1007/s10618-022-00823-6⟩. ⟨hal-03599214⟩
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